Assimilation of AHI Infrared Radiance Measurements for Improved Typhoon Track and Intensity Forecasts Using HWRF

The Advanced Himawari Imager (AHI) onboard the new generation of Japanese geostationary meteorological satellite provides observations of visible, near-infrared and infrared with much improved vertical, horizontal and temporal resolutions than any previously launched geostationary satellites. In order to properly assimilate AHI data in numerical weather prediction (NWP) data assimilation systems, bias estimate, cloud detection and surface emissivity modeling are three key components that must be investigated. In this study, the biases of the AHI brightness temperature observations from the model simulations are firstly characterized and evaluated using both Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). Secondly, an infrared only cloud mask (CM) algorithm that could identify cloud-contaminated pixels without involving any visible or near-infrared channels is developed. Thirdly, the performances of three surface emissivity models embedded in the CRTM for the simulations of the Advanced Himawari Imager (AHI) surface-sensitive infrared channels over different surface types are assessed. Finally, the AHI observations are assimilated into the Hurricane Weather Research and Forecasting (HWRF) system through the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) system. Improvements were made to bias correction (BC) and quality control (QC) of AHI data. Numerical results showed positive impacts of AHI radiance assimilation on typhoon track and intensity forecasts as well as the importance of satellite QC and BC algorithms on maximizing the role of AHI imager radiances for typhoon forecasts using the HWRF.